{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "code_folding": [],
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import numpy.ma as ma\n",
    "\n",
    "from numpy.random import uniform, seed\n",
    "\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "import matplotlib\n",
    "from matplotlib import cm\n",
    "# This import registers the 3D projection, but is otherwise unused.\n",
    "from mpl_toolkits.mplot3d import Axes3D  # noqa: F401 unused import\n",
    "from matplotlib.collections import PolyCollection\n",
    "from matplotlib import colors as mcolors\n",
    "\n",
    "from scipy.interpolate import CubicSpline, UnivariateSpline, griddata\n",
    "from scipy.signal import savgol_filter\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "def bin_data(xi, yi):\n",
    "    x = np.unique(xi)\n",
    "    y = [None] * x.size\n",
    "    for i in range(x.size): y[i] = yi[xi == x[i]]\n",
    "    # Return \n",
    "    y_mean = np.array([a.mean() for a in y])\n",
    "    y_err = np.array([np.std(a) / (a.size**0.5) for a in y])\n",
    "    return (x, y_mean, y_err, y, xi, yi)\n",
    "\n",
    "\n",
    "\n",
    "def cc(arg):\n",
    "    '''\n",
    "    Shorthand to convert 'named' colors to rgba format at 60% opacity.\n",
    "    '''\n",
    "    return mcolors.to_rgba(arg, alpha=0.6)\n",
    "\n",
    "\n",
    "def polygon_under_graph(xlist, ylist):\n",
    "    '''\n",
    "    Construct the vertex list which defines the polygon filling the space under\n",
    "    the (xlist, ylist) line graph.  Assumes the xs are in ascending order.\n",
    "    '''\n",
    "    return [(xlist[0], 0.), *zip(xlist, ylist), (xlist[-1], 0.)]\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def Xnn_app1(omega,k,c1,c2,D1,D2,alpha,Gamma):\n",
    "    Denorminator=(omega**2-(c1**2*k**2)+1j*D1*k**2*omega)*(omega**2-(c2**2*k**2)+1j*D2*k**2*omega)\n",
    "    Numberator=omega**2+(1j*Gamma*k**2*omega)-alpha*(c1**2)*(c2**2)*k**2\n",
    "    return k**2*Numberator/Denorminator \n",
    "\n",
    "def XTn_app1(omega,k,c1,c2,D1,D2,alpha,Gamma):\n",
    "    Denorminator=(omega**2-(c1**2*k**2)+1j*D1*k**2*omega)*(omega**2-(c2**2*k**2)+1j*D2*k**2*omega)\n",
    "    Numberator=-(1j*Gamma*k**2*omega)+alpha*(c1**2)*(c2**2)*k**2\n",
    "    return k**2*Numberator/Denorminator \n",
    "\n",
    "def ImXnn_app1(omega,k,c1,c2,D1,D2,alpha,Gamma):\n",
    "    return np.imag(Xnn_app1(omega,k,c1,c2,D1,D2,alpha,Gamma))\n",
    "\n",
    "def ImXTn_app1(omega,k,c1,c2,D1,D2,alpha,Gamma):\n",
    "    return np.imag(XTn_app1(omega,k,c1,c2,D1,D2,alpha,Gamma))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.interpolate import interp1d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "colorstyle=dict(red_face = np.array([255,85,65])/255, red_edge = np.array([201,67,52])/255,\n",
    "                blue_face = np.array([49,115,255])/255, blue_edge = np.array([36,85,189])/255,\n",
    "                green_face= np.array([84,224,81])/255, green_edge= np.array([62,166,60])/255,\n",
    "                yellow_face=np.array([255,207,49])/255,yellow_edge=np.array([191,155,36])/255,\n",
    "                gray_face=np.array([169,169,169])/255,gray_edge=np.array([137,137,137])/255)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# import"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## read response functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all=pd.read_pickle('Fig3CD.pkl')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "SelectedNames=['2021-01-06-DS7','2021-01-06-DS2','2021-01-06-DS6','2019-08-19-DS2','2021-01-06-DS5','2021-01-06-DS4']\n",
    "\n",
    "# SelectedNames=['2021-01-06-DS2','2021-01-06-DS6','2019-08-19-DS2','2021-01-06-DS5','2021-01-06-DS4']\n",
    "# SelectedNames=['2021-01-06-DS7','2021-01-06-DS2','2021-01-06-DS6','2021-01-06-DS5','2021-01-06-DS4']\n",
    "\n",
    "index_list=len(SelectedNames)*[None]\n",
    "\n",
    "for i,r in enumerate(SelectedNames):\n",
    "    ind=df_all.DS_name.tolist().index(r)\n",
    "    index_list[i]=ind\n",
    "    \n",
    "EF_mean=np.mean(df_all.EF[index_list])\n",
    "k1mean=np.mean(df_all.kTilde)\n",
    "\n",
    "\n",
    "EF_show=df_all.iloc[index_list[3]].EF\n",
    "kTilde_show=df_all.iloc[index_list[3]].kTilde"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## read sample profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.io as sio\n",
    "\n",
    "DS_read_Sono='Fig3AB'\n",
    "Data_read=sio.loadmat(DS_read_Sono+'.mat',squeeze_me=True)\n",
    "\n",
    "\n",
    "Sono_TTilde_show=Data_read['Sono_TTilde']\n",
    "Sono_nTilde_show=Data_read['Sono_density']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TTilde_show=0.125\n",
    "DTTilde_show=Sono_TTilde_show[2]\n",
    "DS_show=DTTilde_show/TTilde_show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## colormap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib import colors as mcolors\n",
    "colorsraw = [\n",
    "    (10, 49, 244),\n",
    "#     (130,75,111),\n",
    "    (252, 30, 32)\n",
    "]\n",
    "colors = [[c/255 for c in cl] for cl in colorsraw]\n",
    "\n",
    "cmapbuild = mcolors.LinearSegmentedColormap.from_list('colormap', colors)\n",
    "\n",
    "def cmapscaled2(temp, cscale, alpha, saturation=0.7, lightness=0.9):\n",
    "#     return [*cmapbuild(temp/cscale)[:2],alpha]\n",
    "    temp=temp-.07\n",
    "    originalhsv = np.array(mcolors.rgb_to_hsv(cmapbuild(temp/cscale)[:3]))\n",
    "    modifier = np.array([0,saturation,lightness])\n",
    "    newrgb = mcolors.hsv_to_rgb(originalhsv-modifier)\n",
    "    return [*newrgb[:3], alpha]\n",
    "\n",
    "cmapscaled = cmapscaled2\n",
    "\n",
    "cscale=1\n",
    "\n",
    "fillcmap = lambda temp: cmapscaled(temp, cscale, 0.2, 0, 0.1)\n",
    "facecmap = lambda temp: cmapscaled(temp, cscale, 1, 0.4,.010)\n",
    "edgecmap = lambda temp: cmapscaled(temp, cscale, 1, 0.1,0.025)\n",
    "linecmap = lambda temp: cmapscaled(temp, cscale, 0.9,0.1,.025)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import cm\n",
    "from matplotlib.colors import ListedColormap, LinearSegmentedColormap\n",
    "\n",
    "bwr_cmap = cm.get_cmap('bwr', 256)\n",
    "\n",
    "a=0.4\n",
    "\n",
    "sclae=a*np.linspace(0, 1, 256)+(1-a)*(((np.linspace(0, 1, 256)-0.5)*2)**3/2+0.5)\n",
    "\n",
    "plt.plot(sclae)\n",
    "\n",
    "bwr_nc = bwr_cmap(sclae)\n",
    "bwr_nc = ListedColormap(bwr_nc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Figure3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdoAAAFOCAYAAAAhE9z6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0SqKoihKAfl/67Buyu8reIwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 486x486 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_offset=0.07\n",
    "box_height_top=0.16\n",
    "box_height_bottom=0.35\n",
    "box_width=0.39\n",
    "box_spacing_v=0.04\n",
    "box_spacing_h=0.085\n",
    "\n",
    "cb_width=0.012\n",
    "cb_spacing=0.008\n",
    "\n",
    "dS_spacing=0.15\n",
    "n_spacing=0.15\n",
    "\n",
    "zslinecol=colorstyle['blue_edge']\n",
    "zsfacecol=colorstyle['blue_face']\n",
    "\n",
    "\n",
    "omega_fitted_show=np.linspace(0,0.005,500)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "fig,axs=plt.subplots(ncols=2,nrows=2,figsize=[6.75,6.75])\n",
    "\n",
    "axs[0,0].set_position([x_offset,0.08+box_spacing_v+box_height_bottom,box_width,box_height_top])\n",
    "axs[0,1].set_position([x_offset+box_spacing_h+box_width,0.08+box_spacing_v+box_height_bottom,box_width,box_height_top])\n",
    "axs[1,0].set_position([x_offset,0.08,box_width,box_height_bottom])\n",
    "axs[1,1].set_position([x_offset+box_spacing_h+box_width,0.08,box_width,box_height_bottom])\n",
    "\n",
    "axs[0,0].tick_params(axis='both',direction='out',right=0,top=0,length=1.5,labelsize=9,pad=2)\n",
    "axs[0,0].set_xticklabels([])\n",
    "axs[0,0].set_xlim([10,100])\n",
    "\n",
    "axs[0,1].tick_params(axis='both',direction='out',right=0,top=0,length=1.5,labelsize=9,pad=2)\n",
    "axs[0,1].set_xticklabels([])\n",
    "axs[0,0].set_xlim([10,100])\n",
    "\n",
    "axs[1,0].tick_params(axis='both',direction='out',right=0,top=0,length=1.5,labelsize=9,pad=2)\n",
    "axs[1,1].tick_params(axis='both',direction='out',right=0,top=0,left=1,length=1.5,labelsize=9,pad=2)\n",
    "\n",
    "ax_cb_T=fig.add_axes([x_offset,0.08+box_spacing_v+box_height_top+box_height_bottom+cb_spacing,box_width,cb_width])\n",
    "\n",
    "ax_cb_n=fig.add_axes([x_offset++box_spacing_h+box_width,0.08+box_spacing_v+box_height_top+box_height_bottom+cb_spacing,box_width,cb_width])\n",
    "\n",
    "\n",
    "plt.sca(axs[0,0])\n",
    "# plt.pcolor(*Sono_TTilde_show, cmap='bwr',linewidth=0)\n",
    "# plt.pcolor(Sono_TTilde_show[0]/(2*df_all.iloc[10].EF)*2*EF_mean,Sono_TTilde_show[1],Sono_TTilde_show[2], cmap=bwr_nc,linewidth=0)\n",
    "plt.pcolor(np.concatenate([Sono_TTilde_show[0]*(k1mean/kTilde_show)*(EF_mean/EF_show),[100]]),np.concatenate([Sono_TTilde_show[1],[109]]),DS_show, cmap=bwr_nc,linewidth=0)\n",
    "\n",
    "plt.clim([-0.103,0.103])\n",
    "plt.ylabel('z ($\\mu$m)',fontsize=9,labelpad=1)\n",
    "\n",
    "ax_cb_T.tick_params(axis='both',direction='in',labelsize=9,length=1.5,top=True,bottom=False,pad=1)\n",
    "cb_T=plt.colorbar(cax=ax_cb_T,orientation='horizontal')\n",
    "\n",
    "ax_cb_T.xaxis.set_label_position('top') \n",
    "ax_cb_T.xaxis.set_ticks_position('top')\n",
    "\n",
    "# plt.title(r'$\\Delta\\ (T/T_{F})$',fontsize=7,pad=13)\n",
    "cb_T.set_ticks([-0.1,0.1])\n",
    "ax_cb_T.set_xlabel(r'Entropy $\\Delta s$',fontsize=12,labelpad=-7)\n",
    "\n",
    "plt.ylim([0,92])\n",
    "plt.yticks([0,45,90])\n",
    "plt.xlim([10,100])\n",
    "\n",
    "ax_cb_T.xaxis.set_ticklabels([r'$-0.1\\,c_V$',r'$0.1\\,c_V$'])\n",
    "\n",
    "\n",
    "plt.sca(axs[0,1])\n",
    "plt.pcolor(np.concatenate([Sono_nTilde_show[0]*(k1mean/kTilde_show)*(EF_mean/EF_show),[100]]),np.concatenate([Sono_nTilde_show[1],[109]]),Sono_nTilde_show[2], cmap='BrBG_r',linewidth=0)\n",
    "plt.clim([-0.15,0.15])\n",
    "# plt.ylabel('z ($\\mu$m)',labelpad=-3)\n",
    "# axs[0,1].set_yticklabels([])\n",
    "# plt.title(r'$\\Delta n/n_0$',fontsize=7,pad=13)\n",
    "plt.ylim([0,92])\n",
    "plt.yticks([0,45,90])\n",
    "plt.ylabel('z ($\\mu$m)',fontsize=9,labelpad=1)\n",
    "plt.xlim([10,100])\n",
    "\n",
    "ax_cb_n.tick_params(axis='both',direction='in',labelsize=9,length=1.5,pad=1)\n",
    "cb_n=plt.colorbar(cax=ax_cb_n,orientation='horizontal')\n",
    "cb_n.set_ticks([-0.1,0.1])\n",
    "ax_cb_n.xaxis.set_label_position('top') \n",
    "ax_cb_n.xaxis.set_ticks_position('top')\n",
    "ax_cb_n.set_xlabel(r'Density $\\Delta n$',fontsize=12,labelpad=-7)\n",
    "\n",
    "\n",
    "ax_cb_n.xaxis.set_ticklabels([r'$-0.1\\,n_0$',r'$0.1\\,n_0$'])\n",
    "\n",
    "plt.sca(axs[1,0])\n",
    "\n",
    "Tstr=['']*len(index_list)\n",
    "\n",
    "for j,i in enumerate(index_list):\n",
    "    r=df_all.iloc[i]\n",
    "    plt.errorbar(r.Delta_TTilde[0]*(k1mean/r.kTilde)*(EF_mean/r.EF),r.Delta_S+dS_spacing*j,yerr=r.Delta_S_err,marker='o',linestyle='None',markersize=2,\n",
    "                mec=linecmap(j/(len(index_list)-1)),ecolor=linecmap(j/(len(index_list)-1)),elinewidth=0.75,mew=0.75,mfc=facecmap(j/(len(index_list)-1)))\n",
    "#     plt.plot(r.Delta_TTilde[0],r.Delta_TTilde[1]+dS_spacing*i,'o',markersize=4)\n",
    "    Delta_S_fitted=ImXTn_app1(omega_fitted_show,r.kTilde,r.c1,r.c2,r.D1,r.D2,\n",
    "                           r.alpha_Tn,r.Gamma_Tn)\n",
    "    mask1=omega_fitted_show>r.Frequency_thres\n",
    "    mask2=omega_fitted_show<=r.Frequency_thres\n",
    "    \n",
    "    Delta_S_fitted[mask1]=Delta_S_fitted[mask1]*r.VTilde1/r.TTilde\n",
    "    Delta_S_fitted[mask2]=Delta_S_fitted[mask2]*r.VTilde2/r.TTilde\n",
    "    \n",
    "    plt.plot(omega_fitted_show[mask1]*(k1mean/r.kTilde)*2*EF_mean,Delta_S_fitted[mask1]+dS_spacing*j,\n",
    "            color=linecmap(j/(len(index_list)-1)),linewidth=0.75)\n",
    "    \n",
    "    plt.plot(omega_fitted_show[mask2]*(k1mean/r.kTilde)*2*EF_mean,Delta_S_fitted[mask2]+dS_spacing*j,\n",
    "            color=linecmap(j/(len(index_list)-1)),linewidth=0.75)\n",
    "    \n",
    "    plt.fill_between(omega_fitted_show[mask1]*(k1mean/r.kTilde)*2*EF_mean,Delta_S_fitted[mask1]+dS_spacing*j,0*Delta_S_fitted[mask1]+dS_spacing*j,\n",
    "                    color=facecmap(j/(len(index_list)-1)),alpha=0.2)\n",
    "    plt.fill_between(omega_fitted_show[mask2]*(k1mean/r.kTilde)*2*EF_mean,Delta_S_fitted[mask2]+dS_spacing*j,0*Delta_S_fitted[mask2]+dS_spacing*j,\n",
    "                    color=facecmap(j/(len(index_list)-1)),alpha=0.2)\n",
    "    \n",
    "    Tstr[j]='{:.2f}'.format(r.TTilde/0.167)\n",
    "    text_temp='$T/T_c=${:.2f}'.format(r.TTilde/0.167)\n",
    "    \n",
    "    plt.text(47.5,dS_spacing*j+0.037,text_temp,fontsize=9)\n",
    "plt.text(-0.03,1.027,'2nd Sound',fontsize=12,transform=axs[1,0].transAxes)\n",
    "plt.text(0.7,1.027,'1st Sound',fontsize=12,transform=axs[1,0].transAxes)\n",
    "\n",
    "\n",
    "\n",
    "plt.ylim([-dS_spacing*0.5,dS_spacing*(-0.1+len(index_list))])\n",
    "plt.ylabel(r'${\\Delta} s$',fontsize=9,labelpad=3)\n",
    "plt.xlim([10,100])\n",
    "plt.xlabel(r'$\\omega/2\\pi$ (Hz)',fontsize=9,labelpad=0)\n",
    "plt.yticks([])\n",
    "\n",
    "# yTlabels=['']*len(np.arange(0,(n_spacing*len(index_list)),n_spacing))\n",
    "# yTlabels[0]='0'\n",
    "# yTlabels[1]=str(dS_spacing)\n",
    "\n",
    "plt.grid(linewidth=0.25)\n",
    "axs[1,0].yaxis.set_label_coords(-0.025,0.44)\n",
    "\n",
    "axs[1,0].grid(axis='y')\n",
    "plt.yticks([0,0.1])\n",
    "axs[1,0].set_yticklabels(['0',r'$0.1\\,c_V$'])\n",
    "\n",
    "\n",
    "\n",
    "plt.sca(axs[1,1])\n",
    "\n",
    "for j,i in enumerate(index_list):\n",
    "    r=df_all.iloc[i]\n",
    "#     plt.plot(r.Delta_n[0],-r.Delta_n[1]+n_spacing*i,'o',markersize=4)\n",
    "    plt.errorbar(r.Delta_n[0]*(k1mean/r.kTilde)*(EF_mean/r.EF),r.Delta_n[1]+n_spacing*j,yerr=r.Delta_n_err,marker='o',linestyle='None',markersize=2,\n",
    "                mec=linecmap(j/(len(index_list)-1)),ecolor=linecmap(j/(len(index_list)-1)),elinewidth=0.75,mew=0.75,mfc=facecmap(j/(len(index_list)-1)))\n",
    "    \n",
    "    Delta_n_fitted=ImXnn_app1(omega_fitted_show,r.kTilde,r.c1,r.c2,r.D1,r.D2,\n",
    "                           r.alpha_nn,r.Gamma_nn)\n",
    "    mask1=omega_fitted_show>r.Frequency_thres\n",
    "    mask2=omega_fitted_show<=r.Frequency_thres\n",
    "    \n",
    "    Delta_n_fitted[mask1]=Delta_n_fitted[mask1]*r.VTilde1\n",
    "    Delta_n_fitted[mask2]=Delta_n_fitted[mask2]*r.VTilde2\n",
    "    \n",
    "    plt.plot(omega_fitted_show*(k1mean/r.kTilde)*2*EF_mean,-Delta_n_fitted+n_spacing*j,color=linecmap(j/(len(index_list)-1)),linewidth=0.75)\n",
    "    \n",
    "    plt.fill_between(omega_fitted_show*(k1mean/r.kTilde)*2*EF_mean,-Delta_n_fitted+n_spacing*j,0*Delta_n_fitted+n_spacing*j,\n",
    "                    color=facecmap(j/(len(index_list)-1)),alpha=0.2)\n",
    "    \n",
    "plt.xlim([10,100])\n",
    "plt.ylabel(r'$-\\Delta n$',fontsize=9,labelpad=3)\n",
    "plt.xlabel(r'$\\omega/2\\pi$ (Hz)',fontsize=9,labelpad=0)\n",
    "\n",
    "plt.ylim([-n_spacing*0.5,n_spacing*(-0.1+len(index_list))])\n",
    "plt.grid(linewidth=0.25)\n",
    "plt.text(-0.03,1.027,'2nd Sound',fontsize=12,transform=axs[1,1].transAxes)\n",
    "plt.text(0.7,1.027,'1st Sound',fontsize=12,transform=axs[1,1].transAxes)\n",
    "\n",
    "axs[1,1].yaxis.set_label_position(\"left\")\n",
    "\n",
    "\n",
    "axs[1,1].yaxis.set_label_coords(-0.025,0.44)\n",
    "\n",
    "plt.yticks([0,n_spacing])\n",
    "\n",
    "\n",
    "axs[1,1].grid(axis='y')\n",
    "axs[1,1].set_yticklabels(['0',r'$0.1\\,n_0$'])\n",
    "\n",
    "\n",
    "fig.savefig('Fig3.pdf',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {
    "height": "120px",
    "width": "252px"
   },
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "755px",
    "left": "0px",
    "right": "1112px",
    "top": "107px",
    "width": "212px"
   },
   "toc_section_display": "block",
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
